Multi-view Rank Pooling for 3D Object Recognition**

3D shape recognition via deep learning is drawing more and more attention due to huge industry interests. As 3D deep learning methods emerged, the view-based approaches have gained considerable success in object classification. Most of these methods focus on designing a pooling scheme to aggregate CNN features of multi-view images into a single compact one. However, these view-wise pooling techniques suffer from loss of visual information. To deal with this issue, an adaptive rank pooling layer is introduced in this paper. Unlike max-pooling which only considers the maximum or mean-pooling that treats each element indiscriminately, the proposed pooling layer takes all the elements into account and dynamically adjusts their importances during the training. Experiments conducted on ModelNet40 and ModelNet10 shows both efficiency and accuracy gain when inserting such a layer into a baseline CNN architecture.